BRIEF REPORT
Vaccination Rates are Associated With Functional
Proximity But Not Base Proximity of Vaccination Clinics
John Beshears, PhD,* James J. Choi, PhD,w David I. Laibson, PhD,z
Brigitte C. Madrian, PhD,y and Gwendolyn I. Reynolds, MTS8
Background: Routine annual influenza vaccinations are recommended for persons 6 months of age and older, but less than half of
US adults get vaccinated. Many employers offer employees free
influenza vaccinations at workplace clinics, but even then take-up is
low.
Objective: To determine whether employees are significantly more
likely to get vaccinated if they have a higher probability of walking
by the clinic for reasons other than vaccination.
Method: We obtained data from an employer with a free workplace
influenza vaccination clinic. Using each employee’s building entry/
exit swipe card data, we test whether functional proximity—the
likelihood that the employee walks by the clinic for reasons other
than vaccination—predicts whether the employee gets vaccinated at
the clinic. We also test whether base proximity—the inverse of
walking distance from the employee’s desk to the clinic—predicts
vaccination probability.
Participants: A total of 1801 employees of a health benefits administrator that held a free workplace influenza vaccination clinic.
Results: A 2 SD increase in functional proximity is associated with
a 6.4 percentage point increase in the probability of vaccination
(total vaccination rate at company = 40%), even though the average
From the *Harvard Business School, Harvard University and National Bureau of Economic Research, Boston, MA; wYale University and National
Bureau of Economic Research, New Haven, CT; zDepartment of Economics; yHarvard Kennedy School, Harvard University and National
Bureau of Economic Research; and 8National Bureau of Economic
Research, Cambridge, MA.
The findings and conclusions expressed are solely those of the authors and
do not represent the views of the NIH or the NBER.
D.I.L. has served as a member of the Express Scripts Scientific Advisory
Board since 2008. In 2010, D.I.L. donated all of these proceeds to
charity. Since 2011, Express Scripts has directly donated to charity all
compensation that he would have received. The remaining authors declare no conflict of interest.
Reprints: Gwendolyn I. Reynolds, MTS, National Bureau of Economic
Research, 1050 Massachusetts Avenue, Cambridge, MA 02138. E-mail:
[email protected].
Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF
versions of this article on the journal’s Website, www.lww-medical
care.com.
Copyright r 2016 Wolters Kluwer Health, Inc. All rights reserved. This is
an open-access article distributed under the terms of the Creative Commons
Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND),
where it is permissible to download and share the work provided it is properly
cited. The work cannot be changed in any way or used commercially.
ISSN: 0025-7079/16/5406-0578
578 | www.lww-medicalcare.com
employee’s desk is only 166 meters from the clinic. Base proximity
does not predict vaccination probability.
Conclusions and Relevance: Minor changes in the environment
can have substantial effects on the probability of vaccination. If
these results generalize, health systems should emphasize functional
proximity over base proximity when locating preventive health
services.
Key Words: vaccination, clinics, workplace wellness, proximity,
preventive care, influenza
(Med Care 2016;54: 578–583)
T
he annual economic cost of influenza-attributable illness
for adults ages 18 and over is estimated to be $87.1
billion–$10.4 billion in direct medical costs, $16.3 billion in
lost earnings, and $60.4 billion in lost statistical lives.1
Routine annual influenza vaccination is recommended for
all persons 6 months of age and older without contraindications,2 but only 46% of adults over 18 years of age
were vaccinated in the 2011–2012 influenza season.3 Twenty
percent of adults ages 18–64 who receive an influenza vaccination receive it at their workplace.4 However, less than
half of employees with access to a free workplace influenza
vaccination clinic are vaccinated.5
One common approach to increasing the use of preventive health care services is to reduce the physical distance
between the individual’s base location and the health care
facility, so that obtaining health care is less burdensome. But
previous literature has reached conflicting conclusions about
the relationship between distance from an individual’s home
to health care facilities and usage of health care services.6–11
Other research has found that forgetfulness or a failure to
plan is partially responsible for the low take-up of vaccinations and other preventive health behaviors.5,12,13 Therefore, we hypothesized that the likelihood of visiting a free
workplace influenza vaccination clinic would be greater
among individuals who have a higher probability of walking
by the clinic for reasons other than vaccination and thus
being reminded of the vaccination opportunity. Furthermore,
we hypothesized that this probability would be a more
powerful predictor of vaccination than the distance between
the employee’s desk and the clinic.
Using desk location information and employees’ building entry/exit swipe card data from a company that offered a
free 2-day worksite influenza vaccination clinic, we separately
identify the vaccination effects of base proximity—the inverse
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Volume 54, Number 6, June 2016
of walking distance between one’s desk and the clinic—and
functional proximity—the likelihood of passing near the clinic
during the course of a normal work day (ie, days when the
clinic is not open).
METHODS
Study Design
We study the 2011 influenza vaccine uptake of employees at the headquarters of a health benefits administrator
in the United States. These employees are generally not
health care personnel. All of them have health insurance. Of
the company’s total workforce (including those not based at
the headquarters), 26% are African Americans and 37% are
racial minorities.
There are 2 main buildings at the company headquarters. Building One houses 520 employees and is the site
of the vaccination clinic; Building Two houses 1281 employees. The 2 buildings are 131 meters apart and connected
by an enclosed passageway. The clinic was located near the
cafeteria in Building One and adjacent to the passageway
connecting the 2 buildings. The clinic was conducted from
October 19 to 20, 2011, and it was advertised during the 3
weeks prior. Figure 1 shows a stylized diagram of the 2
buildings and the passageway, as well as the location of the
clinic.
The company requires employees to swipe a personalized electronic badge to open the external doors of its
buildings, which include the doors to the passageway between the buildings. The company provided us data on the
date and time of each swipe in September and October 2011.
If an employee swipes her badge and holds the door open for
another employee, we do not observe that other employee.
The badge swipe data are therefore an incomplete measure of
all movements between the buildings. The company also
gave us data on employee characteristics and vaccination
uptake, scaled architectural plans of the buildings, and employee desk maps.
Predictive Variables and Hypotheses
We use the frequency of an employee’s badge swipes
for entry into Building One at the end of the passageway
from Building Two to create proxies for an employee’s
“functional proximity” to the clinic. Recall that this door is
adjacent to the clinic location, and employees in Building
Two who did not walk outdoors had to use this door to reach
Flu Shot Clinic
Building Two
Building One
Tunnel & Link Doors
FIGURE 1. Stylized building layout and clinic location.
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Vaccination Rates and Proximity to Clinics
the clinic. Therefore, we believe that the badge swipe data
capture a high fraction of Building Two employees’ visits to
the clinic location. In contrast, employees in Building One
did not have to use this door to access the clinic, as the clinic
was in Building One. Badge swipe data consequently capture
a smaller fraction of Building One employees’ visits to the
clinic location. In sum, badge swipes measure functional
proximity to the clinic much more accurately for employees
in Building Two than for employees in Building One. Hence,
we expect attenuation bias from measurement error to affect
our estimates of functional proximity’s effect on vaccination
much more severely for Building One employees than for
Building Two employees.
We would expect a mechanical relationship between
the number of badge swipes on clinic days and vaccination,
as most Building Two employees who were vaccinated
swiped to get to the clinic. Therefore, we construct our
functional proximity measures using the number of badge
swipes during only the 59 nonclinic days in September and
October—that is, excluding October 19–20. We do not use
the months before September because the number of badge
swipes during the summer is more likely to be affected by
vacations, making them less reflective of routines while in
the office. We exclude months after October to keep reverse
causality from affecting our results; those who get vaccinated
might have more badge swipes in subsequent months because they are not home sick with influenza. The last 10 days
of October do not create such reverse causality concerns
because it takes about 2 weeks after vaccination for immunity to develop.14
We create 3 measures: the number of badge swipes on
all nonclinic days (including weekends, when the business
was not officially open but the building was accessible to
employees), the number of badge swipes on nonclinic
weekdays from 9 AM to 2:30 PM (the hours that the clinic was
open on clinic days), and the number of badge swipes on
nonclinic weekdays before 9 AM and after 2:30 PM (the hours
that the clinic was closed on clinic days).
We had 3 functional proximity hypotheses:
Hypothesis 1 (H1): For employees based in Building Two
(where the clinic was not located), the number of badge
swipes for the entry door to Building One on nonclinic
days will be positively associated with vaccination.
Hypothesis 2 (H2): For employees based in Building Two,
the number of badge swipes for the entry door to Building
One on nonclinic days from 9 AM to 2:30 PM (clinic hours)
will be more predictive of vaccination than the number of
badge swipes for the entry door on nonclinic days before
9 AM and after 2:30 PM (nonclinic hours).
Hypothesis 3 (H3): For employees based in Building One
(where the clinic was located), the number of badge
swipes for the entry door to Building One on nonclinic
days will not be associated with vaccination, regardless of
the time of day.We also measure the minimum walking
distance from each employee’s desk to the clinic using the
architectural plans of Buildings One and Two. Vertical
distance is excluded from this measure, although horizontal distance to any necessary stairs is included. We test
whether “base proximity” (the reciprocal of minimum
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Beshears et al
walking distance in meters) is associated with flu shot
uptake.
Hypothesis 4 (H4): For Building Two employees, base
proximity will be a weaker predictor of vaccination than
the number of badge swipes.
We use the reciprocal of walking distance to reduce the
possible impact of outliers whose desk is very far away from
the clinic. However, using walking distance as our measure
of base proximity yields similar results.
Andersen’s15 Behavioral Model of Health Services
Use identifies 3 components that drive health service utilization: the predisposition to use services, factors that enable
the use of services, and the need for services. Our study
focuses on the location of health services, which is a key
enabling factor, along 2 dimensions: functional proximity
and base proximity. Predisposing characteristics that incline
an individual to get an influenza vaccination that have been
documented in the literature include age, sex, race, education, socioeconomic status, insurance status, prior experience
with influenza vaccination, beliefs about the efficacy and
side effects of vaccination, and predictions of the percentage
of coworkers who will be vaccinated.16,17 Of these, our data
allow us to control directly for age and sex, and we know that
all employees in our sample have health insurance. We are
also able to control for 12 binary variables indicating which
of the company’s 12 job grades—which are related to job
title and description—maps to the individual; 3 binary variables for if the worker is full-time, a regular hire (rather than
temporary), or salaried (rather than hourly); and a binary
variable for whether the employee has an office instead of a
cubicle (indicating higher job status). Job characteristics will
be correlated with the employee’s education, race, and socioeconomic status. If controlling for job characteristics
causes the coefficients on functional proximity and base
proximity to attenuate significantly, this would raise concern
that the absence of direct education, race, etc. controls is
responsible for any significant proximity effects that we find.
The absence of other predisposing, enabling, and need
characteristics in our data biases our study’s estimates of
proximity effects only to the extent that these unobserved
variables are correlated with functional or base proximity.
We additionally control for binary variables indicating
on which building floor level the employee’s desk is located
to correct for the exclusion of vertical distance from our base
proximity measure. Age, sex, and job grade data are occasionally missing for an employee. We correct for this
through 3 binary variables indicating whether age, sex, or job
Volume 54, Number 6, June 2016
grade is missing. The age, sex, or job grade variable values
are set to zero when the relevant data are missing.
Statistical Analysis
Our initial descriptive analysis of the main variables
consists of computing their means and (where relevant) SDs.
We also report P-values from tests of whether these variables’ means differ between Building One and Building Two.
In our main analysis, we run regressions separately by
building to evaluate the impact of employees’ proximity to
the clinic on their likelihood of receiving an influenza vaccination. For each building’s regressions, we standardize our
2 proximity measures to each have zero mean and unit
variance within the building. To ease the interpretation of
marginal effects from the regression coefficients, we use a
linear probability model (ie, an ordinary least squares regression with a binary indicator for receiving a vaccination
as the dependent variable), which provides the linear approximation to the conditional expectation function that
minimizes the mean squared prediction error. Linear probability models do not rely upon the strong functional and
distributional assumptions of logit and probit regressions,
and are in this sense more robust.18 Our results are similar
when estimated using logit regressions, as shown in Supplemental Digital Content Tables A1 (Supplemental Digital
Content 1, http://links.lww.com/MLR/B130) and A2 (Supplemental Digital Content 2, http://links.lww.com/MLR/
B131). All analyses were run using Stata version 13.1 (Stata
Corporation, College Station, TX).
RESULTS
Table 1 shows summary statistics for employees in
each building. In Building One, 38% of employees received
an influenza vaccination, compared with 41% of employees
in Building Two. The mean distance to the clinic for employees in Building One is 69.2 meters, compared with 205.1
meters for employees in Building Two. Building One employees swiped their badge 18.22 times on average during
the 59 nonclinic days (ie, 0.31 times/d), of which 10.97
swipes occurred between 9 AM and 2:30 PM on nonclinic
weekdays and 7.10 swipes occurred before 9 AM or after
2:30 PM on nonclinic weekdays. Building Two employees
swiped 4.65 times on average during the nonclinic days, of
which 2.84 occurred between 9 AM and 2:30 PM on nonclinic
weekdays and 1.79 occurred before 9 AM or after 2:30 PM on
nonclinic weekdays.
TABLE 1. Summary Statistics
Mean (SD)
Vaccination rate (%)
Base distance to clinic
Badge use, September–October nonclinic days
Badge use, September–October nonclinic weekdays, clinic time window
Badge use, September–October nonclinic weekdays, not in clinic time window
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Building One
Building Two
Difference (P)
38.08
69.16 m (25.08)
18.22 swipes (24.72)
10.97 swipes (13.12)
7.10 swipes (13.94)
40.67
205.11 m (22.87)
4.65 swipes (9.27)
2.84 swipes (5.14)
1.79 swipes (5.09)
0.309
0.000
0.000
0.000
0.000
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Medical Care
Volume 54, Number 6, June 2016
Vaccination Rates and Proximity to Clinics
TABLE 2. Regression of Influenza Vaccination Uptake on Base and Functional Proximity Measures for Employees in Building Two
Model 1
Badge use, September–October
nonclinic days, standardized
Badge use, September–October
nonclinic weekdays, clinic time
window, standardized
Badge use, September–October
nonclinic weekdays, not clinic
time window, standardized
Base proximity (1/base distance),
standardized
Additional controls
Observations
F statistic for regression
Prob > F
R2
Model 2
Model 3
Model 4
0.026* (0.014)
P = 0.068
No
1281
3.345
0.068
0.003
Model 5
0.032** (0.013)
P = 0.011
0.033** (0.016)
P = 0.045
0.033** (0.015)
P = 0.031
0.004 (0.018)
P = 0.845
0.002 (0.015)
P = 0.904
No
1281
2.600
0.075
0.004
0.006 (0.014)
P = 0.672
No
1281
0.179
0.672
0.000
0.002 (0.014)
P = 0.893
Yes
1281
29.42
0.000
0.040
0.002 (0.014)
P = 0.894
Yes
1281
78.57
0.000
0.041
This table shows OLS linear probability regression results. The dependent variable is an indicator for being vaccinated. Robust SEs are in parentheses. Additional control
variables are building floor level, age, sex, possession of an office, full-time employee status, regular employee status, salaried employee status, job grade, and indicators for whether
age, sex, and job grade are missing. Two of the 12 job grades are consolidated into a single dummy variable because there is only 1 Building Two employee in each. Two-tailed
statistical significance at the 10%, 5%, and 1% level are indicated by *, **, and ***, respectively.
Table 2 shows coefficients from regressions where the
dependent variable is a binary indicator for getting vaccinated. Each column corresponds to a different regression on
the same sample (all employees in Building Two), with SEs
and P-values below each coefficient point estimate. The only
control variable in column 1 is badge use on September to
October nonclinic days. Column 2 controls instead for badge
use at different times of day during September to October
nonclinic days. Column 3 controls only for base proximity.
Column 4 controls for both badge use on September to
October nonclinic days and base proximity, in addition
to which floor the employee’s desk is on, demographics, and
job characteristics. Column 5 shows the effect of badge use
at different times of day and base proximity with the additional controls. Table 3 shows analogous regression results
for Building One employees, with the same column scheme.
Supplemental Digital Content Tables A3 (Supplemental
Digital Content 3, http://links.lww.com/MLR/B132) and A4
(Supplemental Digital Content 4, http://links.lww.com/MLR/
B133) contain the full set of regression coefficients using
proximity variables that have not been standardized.
Consistent with H1, we find that a 1 SD increase in an
employee’s badge use during nonclinic days in September
and October increases the employee’s vaccination likelihood
by 2.6 percentage points (P = 0.068; Table 2, column 1).
Supporting H2, when we limit badge swipes to only the
hours during nonclinic weekdays when the influenza clinic
would be offered on clinic days (9 AM–2:30 PM), the badge
swipe effect increases in magnitude and is statistically significant (P = 0.045; Table 2, column 2), whereas the effect of
nonclinic weekday badge use outside of the clinic time
window is much smaller and not statistically significant
(P = 0.845; Table 2, column 2).
In contrast, base proximity in Building Two is unrelated
to the vaccination rate (P = 0.672; Table 2, column 3), as hypothesized in H4. When controlling for both proximity measures simultaneously, as well as the floor the employee’s desk
is on, demographic controls, and job characteristics, the
TABLE 3. Regression of Influenza Vaccination Uptake on Base and Functional Proximity Measures for Employees in Building One
Model 1
Badge use, September–October nonclinic days,
standardized
Badge use, September–October nonclinic weekdays,
clinic time window, standardized
Badge use, September–October nonclinic weekdays, not
clinic time window, standardized
Base proximity (1/base distance), standardized
Additional controls
Observations
F statistic for regression
Prob > F
R2
Model 2
Model 3
Model 4
0.015 (0.022)
P = 0.489
0.005 (0.022)
P = 0.812
0.035 (0.028)
P = 0.213
0.018 (0.026)
P = 0.490
No
520
0.480
0.489
0.001
Model 5
No
520
0.788
0.455
0.003
0.022 (0.021)
P = 0.308
No
520
1.042
0.308
0.002
0.007 (0.023)
P = 0.751
Yes
520
17.67
0.000
0.070
0.037 (0.028)
P = 0.193
0.029 (0.026)
P = 0.254
0.005 (0.023)
P = 0.811
Yes
520
15.75
0.000
0.073
This table shows OLS linear probability regression results. The dependent variable is an indicator for being vaccinated. Robust SEs are in parentheses. Additional control
variables are building floor level, age, sex, possession of an office, regular employee status, salaried employee status, job grade, and indicators for whether age and sex are missing.
We do not control for missing job grade because all such employees in Building One are also temporary employees, and we do not control for part-time employee status because
there is only 1 part-time employee in Building One. Two-tailed statistical significance at the 10%, 5%, and 1% level are indicated by *, **, and ***, respectively.
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insignificance of base proximity remains unchanged
(P = 0.893; Table 2, column 4), whereas the badge swipe effect
strengthens in magnitude and significance (P = 0.011; Table 2,
column 4). A 1 SD increase in functional proximity, as
measured by total nonclinic day badge swipes, implies a 3.2
percentage point increase in the probability of vaccination for
employees in Building Two (Table 2, column 4). The fact that
our estimate of the functional proximity effect strengthens
when we include job characteristic controls suggests that it is
not being driven by our inability to control directly for education, race, socioeconomic status, and insurance status. Furthermore, we measure functional proximity with more error
than we do base proximity, making more striking the fact that
our functional proximity measure significantly predicts vaccination, whereas base proximity does not.
The last column of Table 2 shows that even after controlling for base proximity and the other explanatory variables,
the effect of nonclinic weekday badge swipes during the times
when the influenza clinic would be offered (9 AM–2:30 PM) remains statistically significant (P = 0.031; Table 2, column 5),
whereas the effect of weekday badge use outside of the clinic
time window remains statistically insignificant (P = 0.904;
Table 2, column 5).
As noted earlier, badge swipe data for Building One
employees is a poor proxy for functional proximity to the clinic.
Accordingly, total badge swipes for employees in Building One
do not significantly predict vaccinations (P = 0.489 without
additional controls, P = 0.812 with additional controls; Table 3,
columns 1 and 4). Vaccination is not predicted by swipes
during clinic times on nonclinic weekdays (P = 0.213 without
additional controls, P = 0.193 with additional controls; Table 3,
columns 2 and 5) or swipes outside of clinic times on nonclinic
weekdays (P = 0.490 without additional controls, P = 0.254
with additional controls; Table 3, columns 2 and 5). Therefore,
H3 is confirmed. As in Building Two, base proximity in
Building One is unrelated to the probability of vaccination
(P = 0.308; Table 3, column 3), and remains so after adding
additional control variables (P = 0.751; Table 3, column 4).
Volume 54, Number 6, June 2016
self-reports, which are subject to reporting and recall bias. A
number of studies have found that offering influenza vaccinations at the workplace, either at fixed locations or using
mobile vaccination carts, is effective at increasing vaccination
rates.21–28 But because functional and base proximity to the
vaccination facilities were not separately measured, the importance of each factor cannot be separately identified.
Our study uses objective measures of activity space
and base proximity, and we can rule out reverse causality
because our functional proximity measure excludes days on
which the vaccination clinic was operating. We find that
functional proximity to the clinic is associated with increased
vaccination rates. An employee in Building Two who traveled through the door adjacent to the clinic 2 SDs more often
during nonclinic days in September and October was 6.4
percentage points more likely to be vaccinated. In contrast,
base proximity to the vaccination clinic (the inverse of the
distance from one’s desk) is not associated with a higher
likelihood of vaccination. When thinking about the enabling
factors in Andersen’s Behavioral Model of Health Service
Use, our results suggest that functional proximity has more
impact on increasing health care use than base proximity.
Our study has some limitations. Worker base proximity
and functional proximity to the clinic were not randomly assigned, so we cannot completely rule out the possibility that
omitted variables that affect vaccination probability (such as
race, education, beliefs about vaccination efficacy, etc.) are
also correlated with our proximity measures, thus biasing the
estimated relationships between proximity and vaccination
probability. Future research could measure other predisposing,
enabling, and need factors in the studied population so that
these characteristics can be directly controlled for when estimating the enabling effect of proximity. In addition, our data
come from a single company during a single flu vaccination
campaign. Therefore, our results may not generalize to other
populations or to other years where there is a different amount
of public attention placed on the risks of influenza.
DISCUSSION
SUMMARY AND CONCLUSIONS
Close proximity of health care facilities to individuals’
“activity spaces,” the set of locations regularly visited during
the course of daily living, has been hypothesized to be an
enabling resource—in the sense of Andersen15—for receiving
health services. Cromley and Shannon19 hypothesize that
health care facilities’ proximity to activity spaces—that is,
functional proximity—is even more important for facilitating
health care access than their proximity to individuals’ homes.
However, empirical evidence on this hypothesis is limited, in
large part due to difficulties with identifying and measuring
proximity to activity spaces. Nemet and Bailey20 find that
health care utilization is higher among the rural elderly if their
primary health care provider is located within their activity
space. However, they cannot rule out the possibility that this
positive correlation arises due to reverse causality—the individual’s activity space encompasses the physician’s location
because the individual visits the physician frequently. In
addition, they measure activity space through respondent
Using objective measures of functional and base proximity to a workplace influenza vaccination clinic, we find that
the probability of an employee getting vaccinated increases
with functional proximity (the likelihood that the employee
walks by the clinic for reasons other than vaccination) but not
with base proximity (the inverse of walking distance from the
employee’s desk to the clinic). A 2 SD increase in functional
proximity is associated with a 6.4 percentage point increase in
the probability of vaccination, even though the average employee’s desk is only 166 meters from the clinic.
Employers currently administer 20% of influenza vaccinations for adults between the ages of 18 and 64.4 The results
of our study suggest that 1 way to assess the structural quality
of a workplace preventive care clinic is its functional proximity
to employees. Clinics should be placed in a location that
workers frequently walk past, which is not necessarily the
location that is physically closest to workers’ base locations.
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Volume 54, Number 6, June 2016
ACKNOWLEDGMENTS
The authors thank Express Scripts for conducting the
field experiment and providing the data. The authors acknowledge individual and collective financial support from
National Institutes of Health grants P30-AG-034532, and
P01-AG-005842.
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